SoroushVahidi/scidocs huggingface dataset:
About relationship between business text patterns and financial performance in corporate data
(2018)

This study uses text and data mining to investigate the relationship between the text patterns of annual reports published by US listed companies and sales performance. Taking previous research a step further, although annual reports show only past and present financial information, analyzing text content can identify sentences or patterns that indicate the future business performance of a company. First, we examine the relation pattern between business risk factors and current business performance. For this purpose, we select companies belonging to two categories of US SIC (Standard Industry Classification) in the IT sector, 7370 and 7373, which include Twitter, Facebook, Google, Yahoo, etc. We manually collect sales and business risk information for a total of 54 companies that submitted an annual report (Form 10-K) for the last three years in these two categories. To establish a correlation between patterns of text and sales performance, four hypotheses were set and tested. To verify the hypotheses, statistical analysis of sales, statistical analysis of text sentences, sentiment analysis of sentences, clustering, dendrogram visualization, keyword extraction, and word-cloud visualization techniques are used. The results show that text length has some correlation with sales performance, and that patterns of frequently appearing words are correlated with the sales performance. However, a sentiment analysis indicates that the positive or negative tone of a report is not related to sales performance.
Graphical models for text mining: knowledge extraction and performance estimation
(2011)

This thesis would not have been possible unless the constant support of my advisor Professor Fabio Stella. He gave me suggestions, advices and a constant incentive to learn and deeply understand how things works. Special thanks go to Professor Enrico Fagiuoli for its suggestions and its huge knowledge of probabilistic models. internship has been a precious occasion to learn and discuss and to find new friends. Special thanks go my colleagues at DISCo that shared the highs and lows of my Ph.D. In particular, i thank my laboratory colleague Elena Gatti for our discussion and friendship and all the other Ph.D students for the discussion and fun moments. I thanks all the administrative personnel at the department, without them a Ph.D student life is hell. Thanks to all the student who worked with me during this years, part of the work in this thesis has been developed thanks to their contribution. It is a pleasure to thank those who made my Ph.D and thus my thesis possible: Docflow S.p.A. They gave me the possibility to get a sense of business world, their needs and to propose solutions. A special thanks go to all who have tolerated my obsessions and complaints during this years: my family, my friends and Manoela.
Exploring the benefits of the combination of a software architecture analysis and a usability evaluation of a mobile application
(2010)

Designing easy to use mobile applications is a difficult task. In order to optimize the development of a usable mobile application, it is necessary to consider the mobile usage context for the design and the evaluation of the user–system interaction of a mobile application. In our research we designed a method that aligns the inspection method “Software ArchitecTure analysis of Usability Requirements realizatioN” SATURN and a mobile usability evaluation in the form of a user test. We propose to use mobile context factors and thus requirements as a common basis for both inspection and user test. After conducting both analysis and user test, the results described as usability problems are mapped and discussed. The mobile context factors identified define and describe the usage context of a mobile application. We oftware architecture analysis
Script independent scene text segmentation using fast stroke width transform and GrabCut
(2015)

In this paper, a novel script independent scene text segmentation method is presented. The proposed method introduces an approach for character segmentation which combines the advantages of connected component analysis, stroke width analysis and graph cuts. The method was evaluated on a standard dataset, and also on a custom dataset of scene images of text from various Indian scripts. Additionally, a fast implementation of Stroke Width Transform (SWT) is presented here, which improves speed over other implementations by 79%. From our experiments, we have achieved encouraging results on the usefulness of the combination of text/non-text classification with GrabCut. The technique is robust to the language/script of text.
Improving accuracy in E-Textiles as a platform for pervasive sensing
(2013)

Recently Electronic Textile (E-Textile) technology enables the weaving computation and communication components into the clothes that we wear and objects that we interact with every day. E-Textile enables the design and development of a broad range of pervasive sensing systems such as smart bed sheets for sleep posture monitoring, insoles in medical shoes for monitoring plantar pressure distribution, smart garments and sensing gloves. However the low cost of E-Textiles come at the cost of accuracy. In this work we propose an actuator-based method that increases the accuracy of E-Textiles by means of enabling real-time calibration. Our proposed system increases system accuracy by 38.55% on average (maximum 58.4%).
Imbalance in the cloud: An analysis on Alibaba cluster trace
(2017)

To improve resource efficiency and design intelligent scheduler for clouds, it is necessary to understand the workload characteristics and machine utilization in large-scale cloud data centers. In this paper, we perform a deep analysis on a newly released trace dataset by Alibaba in September 2017, consists of detail statistics of 11089 online service jobs and 12951 batch jobs co-locating on 1300 machines over 12 hours. To the best of our knowledge, this is one of the first work to analyze the Alibaba public trace. Our analysis reveals several important insights about different types of imbalance in the Alibaba cloud. Such imbalances exacerbate the complexity and challenge of cloud resource management, which might incur severe wastes of resources and low cluster utilization. 1) Spatial Imbalance: heterogeneous resource utilization across machines and workloads. 2) Temporal Imbalance: greatly time-varying resource usages per workload and machine. 3) Imbalanced proportion of multi-dimensional resources (CPU and memory) utilization per workload. 4) Imbalanced resource demands and runtime statistics (duration and task number) between online service and offline batch jobs. We argue accommodating such imbalances during resource allocation is critical to improve cluster efficiency, and will motivate the emergence of new resource managers and schedulers.
Automatic Collocation Suggestion in Academic Writing
(2010)

In recent years, collocation has been widely acknowledged as an essential characteristic to distinguish native speakers from non-native speakers. Research on academic writing has also shown that collocations are not only common but serve a particularly important discourse function within the academic community. In our study, we propose a machine learning approach to implementing an online collocation writing assistant. We use a data-driven classifier to provide collocation suggestions to improve word choices, based on the result of classification. The system generates and ranks suggestions to assist learners’ collocation usages in their academic writing with satisfactory results. *
Analysis of the MED Oscillation Problem in BGP
(2002)

The Multi Exit Discriminator (MED) attribute of the Border Gateway Protocol (BGP) is widely used to implement “cold potato routing” between autonomous systems. However, the use of MED in practice has led to BGP persistent oscillation. The MED oscillation problem has been described with example configurations and complicated, step-by-step evaluation of dynamic route computations performed at multiple routers. Our work presents the first rigorous analysis of the MED oscillation problem. We employ the Stable Paths Problem (SPP) formalism that allows a static analysis of the interaction of routing policies. We give a formal definition of MED Induced Routing Anomalies (MIRA) and show that, in general, they can span multiple autonomous systems. However, if we assume that the BGP configurations between ASes follows a common model based on customer/provider and peer/peer relationships, then we show that the scope of any MIRA is always contained within a single autonomous system. Contrary to widely held assumptions, we show that a MIRA can occur even in a fully meshed IBGP configuration. We also show that a stable BGP routing may actually violate the stated semantics of the MED attribute.
Math modeling, neuropsychology, and category learning:: Response to B. Knowlton (1999)
(1999)

raising the issue of the implications of findings in neuropsychological subjects for category learning [Knowlton, B. (1999) What can neuropsychology tell us about category learning? Trends Cognit. Sci. 3, 123–124]1. Knowlton and Squire2,3 reported various dissociations between categorization and recognition in normal and amnesic subjects to support their view that multiple memory systems mediate these tasks. The multiple-system view is more complex than a single-system approach is to modeling categorization and recognition. On grounds of parsimony, a question of scientific interest is whether a single-system model can suffice to account for these data. In support of this possibility, we demonstrated that an extremely simple version of a single-system exemplar model accounted in quantitative detail for the bulk of the dissociation findings reported by Knowlton and Squire, as long as plausible assumptions were made regarding parameter differences in memory ability between groups4. At the same time, we acknowledged some remaining challenges for the exemplar model, and concluded our article by stating, ‘Whether or not a full account of Knowlton and Squire’s complete set of dissociation findings is available within the exemplar approach remains a subject for future research, but we feel that we have made an important start’ (Ref. 4, p. 255). We also outlined various directions in which the exemplar model could be extended to meet these challenges. In the first part of her commentary1, Knowlton reiterates these acknowledged limitations of the simple exemplar model and argues against the possible lines of extension that we suggested. Her first point pertains to the dissociation exhibited by the severe amnesic E.P., who despite showing zero ability to discriminate between old and new exemplars in a recognition task, displayed normal performance in a classification task5. We had pointed out that stimulus conditions were dramatically different across the particular recognition and classification tasks in question. In the test phase of the recognition task, presentations of the target item were rare and widely spaced between distractor items. By contrast, in the classification test, members of the target category were frequently presented. We noted that if E.P. had even a small residual N o s o f s k y a n d Z a k i – R e s p o n s e Update Comment
An Introduction to Boosting and Leveraging
(2002)

We provide an introduction to theoretical and practical aspects of Boosting and Ensemble learning, providing a useful reference for researchers in the field of Boosting as well as for those seeking to enter this fascinating area of research. We begin with a short background concerning the necessary learning theoretical foundations of weak learners and their linear combinations. We then point out the useful connection between Boosting and the Theory of Optimization, which facilitates the understanding of Boosting and later on enables us to move on to new Boosting algorithms, applicable to a broad spectrum of problems. In order to increase the relevance of the paper to practitioners, we have added remarks, pseudo code, “tricks of the trade”, and algorithmic considerations where appropriate. Finally, we illustrate the usefulness of Boosting algorithms by giving an overview of some existing applications. The main ideas are illustrated on the problem of binary classification, although several extensions are discussed. 1 A Brief History of Boosting The underlying idea of Boosting is to combine simple “rules” to form an ensemble such that the performance of the single ensemble member is improved, i.e. “boosted”. Let h1, h2, . . . , hT be a set of hypotheses, and consider the composite ensemble hypothesis
High Power Terahertz and Millimeter-Wave Oscillator Design: A Systematic Approach
(2011)

A systematic approach to designing high frequency and high power oscillators using activity condition is introduced. This method finds the best topology to achieve frequencies close to the fmax of the transistors. It also determines the maximum frequency of oscillation for a fixed circuit topology, considering the quality factor of the passive components. Using this technique, in a 0.13 μm CMOS process, we design and implement 121 GHz and 104 GHz fundamental oscillators with the output power of -3.5 dBm and -2.7 dBm, respectively. Next, we introduce a novel triple-push structure to realize 256 GHz and 482 GHz oscillators. The 256 GHz oscillator was implemented in a 0.13 μm CMOS process and the output power of -17 dBm was measured. The 482 GHz oscillator generates -7.9 dBm (0.16 mW) in a 65 nm CMOS process.
Examining micro-payments for participatory sensing data collections
(2010)

The rapid adoption of mobile devices that are able to capture and transmit a wide variety of sensing modalities (media and location) has enabled a new data collection paradigm - participatory sensing. Participatory sensing initiatives organize individuals to gather sensed information using mobile devices through cooperative data collection. A major factor in the success of these data collection projects is sustained, high quality participation. However, since data capture requires a time and energy commitment from individuals, incentives are often introduced to motivate participants. In this work, we investigate the use of micro-payments as an incentive model. We define a set of metrics that can be used to evaluate the effectiveness of incentives and report on findings from a pilot study using various micro-payment schemes in a university campus sustainability initiative.
Clustering in the Presence of Background Noise
(2014)

We address the problem of noise management in clustering algorithms. Namely, issues that arise when on top of some cluster structure the data also contains an unstructured set of points. We consider how clustering algorithms can be “robustified” so that they recover the cluster structure in spite of the unstructured part of the input. We introduce some quantitative measures of such robustness that take into account the strength of the embedded cluster structure as well as the mildness of the noise subset. We propose a simple and efficient method to turn any centroid-based clustering algorithm into a noiserobust one, and prove robustness guarantees for our method with respect to these measures. We also prove that more straightforward ways of “robustifying” clustering algorithms fail to achieve similar guarantees.
Analyzing Resource Behavior Using Process Mining
(2009)

It is vital to use accurate models for the analysis, design, and/or control of business processes. Unfortunately, there are often important discrepancies between reality and models. In earlier work, we have shown that simulation models are often based on incorrect assumptions and one example is the speed at which people work. The “Yerkes-Dodson Law of Arousal” suggests that a worker that is under time pressure may become more efficient and thus finish tasks faster. However, if the pressure is too high, then the worker’s performance may degrade. Traditionally, it was difficult to investigate such phenomena and few analysis tools (e.g., simulation packages) support workload-dependent behavior. Fortunately, more and more activities are being recorded and modern process mining techniques provide detailed insights in the way that people really work. This paper uses a new process mining plug-in that has been added to ProM to explore the effect of workload on service times. Based on historic data and by using regression analysis, the relationship between workload and services time is investigated. This information can be used for various types of analysis and decision making, including more realistic forms of simulation.
Heart rate variability and its relation to prefrontal cognitive function: the effects of training and detraining
(2004)

The aim of the present study was to investigate the relationship between physical fitness, heart rate variability (HRV) and cognitive function in 37 male sailors from the Royal Norwegian Navy. All subjects participated in an 8-week training program, after which the subjects completed the initial cognitive testing (pre-test). The subjects were assigned into a detrained group (DG) and a trained group (TG) based on their application for further duty. The DG withdrew from the training program for 4 weeks after which all subjects then completed the cognitive testing again (post-test). Physical fitness, measured as maximum oxygen consumption (V̇O2max), resting HRV, and cognitive function, measured using a continuous performance task (CPT) and a working memory test (WMT), were recorded during the pre-test and the post-test, and the data presented as the means and standard deviations. The results showed no between-group differences in V̇O2max or HRV at the pre-test. The DG showed a significant decrease in V̇O2max from the pre- to the post-test and a lower resting HRV than the TG on the post-test. Whereas there were no between-group differences on the CPT or WMT at the pre-test, the TG had faster reaction times and more true positive responses on tests of executive function at the post-test compared to the pre-test. The DG showed faster reaction times on non-executive tasks at the post-test compared to the pre-test. The results are discussed within a neurovisceral integration framework linking parasympathetic outflow to the heart to prefrontal neural functions.
Power spectrum analysis of heart rate fluctuation: a quantitative probe of beat-to-beat cardiovascular control.
(1981)

Power spectrum analysis of heart rate fluctuations provides a quantitative noninvasive means of assessing the functioning of the short-term cardiovascular control systems. We show that sympathetic and parasympathetic nervous activity make frequency-specific contributions to the heart rate power spectrum, and that renin-angiotensin system activity strongly modulates the amplitude of the spectral peak located at 0.04 hertz. Our data therefore provide evidence that the renin-angiotensin system plays a significant role in short-term cardiovascular control in the time scale of seconds to minutes.
Vagal influence on working memory and attention.
(2003)

The aim of the present study was to investigate the effect of vagal tone on performance during executive and non-executive tasks, using a working memory and a sustained attention test. Reactivity to cognitive tasks was also investigated using heart rate (HR) and heart rate variability (HRV). Fifty-three male sailors from the Royal Norwegian Navy participated in this study. Inter-beat-intervals were recorded continuously for 5 min of baseline, followed by randomized presentation of a working memory test (WMT) based on Baddeley and Hitch's research (1974) and a continuous performance test (CPT). The session ended with a 5-min recovery period. High HRV and low HRV groups were formed based on a median split of the root mean squared successive differences during baseline. The results showed that the high HRV group showed more correct responses than the low HRV group on the WMT. Furthermore, the high HRV group showed faster mean reaction time (mRT), more correct responses and less error, than the low HRV group on the CPT. Follow-up analysis revealed that this was evident only for components of the CPT where executive functions were involved. The analyses of reactivity showed a suppression of HRV and an increase in HR during presentation of cognitive tasks compared to recovery. This was evident for both groups. The present results indicated that high HRV was associated with better performance on tasks involving executive function.
0.9–10GHz low noise amplifier with capacitive cross coupling
(2010)

This paper presents a 0.9GHz-10GHz Ultra Wideband Low Noise Amplifier (LNA) designed for software-defined-radios (SDR). Capacitive cross coupling (CCC) is used at both input stage and cascade stage for wideband input impedance matching and small noise figure (NF). A combination of inductor peaking load and series inductor between the cascade stages of LNA is employed for a flat gain and enhanced input matching. This LNA is implemented in a standard 0.18µm CMOS technology. For 0.9GHz-10GHz ultra wideband applications, it achieves a maximum gain of 14dB, 3.5dB∼4.1dB NF and +1.8dBm IIP3. It consumes only7.6mW from a 1.8V power supply.
New Direct Torque Control Scheme for BLDC Motor Drives Suitable for EV Applications
(2014)

This paper proposes a simple and effective scheme for Direct Torque Control (DTC) of Brushless DC Motor. Unlike the traditional DTC where both the torque and flux must to be employed in order to deliver the switching table for the inverter, the proposed DTC scheme utilizes only the torque information. By considering the particular operating principle of the motor, the instantaneous torque can be estimated only by using back EMF ratio in an unified differential equation. The simulation results have shown the good performance of the proposed scheme. It can be seen thus a very suitable technique for EV drives which need fast and precise torque response.
An empirical investigation of student adoption model toward mobile e-textbook: UTAUT2 and TTF model
(2016)

Faculty of Economics in Universitas Atma Jaya Yogyakarta (UAJY) has replaced printed textbooks with e-textbooks for its academic activities since 2015. These e-textbooks can be accessed via iPad which is given to each student. Hence, the objective of this study is to test the proposed research model based on integrated UTAUT2 and TTF. Questionnaires were distributed to 326 students in 10 classes. Only junior and sophomore students were eligible to fulfill the questionnaires. The result of the study shows that performance expectancy, effort expectancy, social influence, facilitating condition, and habit have a direct significant relationship on behavioral intention to use mobile e-textbook. The result also shows that both Task technology and technology characteristic positively affect task technology fit. There is also a direct significant relationship between Task technology fit and performance expectancy. We suggest that further studies include experimentation to investigate performance expectancy of mobile e-book. 2) More evaluation should be held in the future in more mature environment. The finding of this study can help policymakers in the Faculty of Economics UAJY to evaluate the policy and formulate better strategy. In addition, these findings can be used by others faculties and universities as mobile e-textbook adoption case reference.